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A class of partially linear transformation models for recurrent gap times

Author

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  • Miao Han
  • Dongxiao Han
  • Liuquan Sun

Abstract

In this article, we propose a general class of partially linear transformation models for recurrent gap time data, which extends the linear transformation models by incorporating non linear covariate effects and includes the partially linear proportional hazards and the partially linear proportional odds models as special cases. Both global and local estimating equations are developed to estimate the parametric and non parametric covariate effects, and the asymptotic properties of the resulting estimators are established. The finite-sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease is provided.

Suggested Citation

  • Miao Han & Dongxiao Han & Liuquan Sun, 2018. "A class of partially linear transformation models for recurrent gap times," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(3), pages 739-766, February.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:3:p:739-766
    DOI: 10.1080/03610926.2017.1313986
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